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Comparing Two and Three Dimensional Optimization of Turbojet Engine with Multi Target Genetic Algorithm

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Abstract (2. Language): 
In this paper, turbojet engine in ideal condition will be optimized by multi target genetic algorithm. The target functions are specific thrust (ST), specific fuel consumption (SFC) and thermal efficiency (ηt) that once will simultaneously be optimized by two by two way and the results will be revealed in the Pareto curves. For the second time these three objective functions will be optimized at the same time. At the end the findings of two by two ways will be compared with the results of three objective functions. Design variables are considered as Mach number and total compressor pressure ratio. The significant relation between objective functions is introduced according to Pareto points. There is no doubt that these functions without using methods are not considerable.
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REFERENCES

References: 

[1] J.S. Arora, Introduction to Optimum Design, McGraw-Hill, New York,
1989.
[2] S.S Rao, Engineering Optimization: Theory and Practice, Wiley, New
York, 1996.
[3] D.E. Goldberg, Genetic-ALgorithems in search, Optimization, and
Machine Learning, Addison-Wesley, Reading, MA, 1989.
[4] T.Back, D.B. Fogel, Z. Hand book of Evolutionary Computation, Oxford
University press,New York/Oxford, 1997.
[5] V. pareto, Cours d'economic politique, Lausanne, Switzerland,
1896.
[6] N. Srinivas, K. Deb, Multiobjective optimization using nondo- minated
sorting in genetic algorithms, Evolutionary Comput.2(3) (1994) 221-248.
[7] C.M. Fonseca, P.J. Fleming, Genetic algorithms for multiobje-ctive
optimization: Formulation,discussion,san Mateo, CA, 1993, pp.416-423.
[8] Jack D. Mattingly, Elements of Gas Turbine Propulsion, McG-raw-Hill
series in mechanical engineering, 1996.
[9] Pareto, coursd's economic politique, Rouge, Lausanne, Switzerland, 1896
[10] A.Oseyezka, Multicriteria optimization for engineering design, in:J.S
Gero(Ed.), Design optimization, Acadamic press, New York, 1985, pp 193-227.
[11] Abido MA, Bakhashwain JM. Optimal VAr dispatch using a Multi
objective evolutionary algorithm. Int J Electr Power Energy Syst
2005;27(1):13–20.
[12] Sailaja kumari M, Maheswarapu S. Enhanced genetic algorithm based
computation technique for multi-objective optimal power flow solution. Int
JElectr Power Energy Syst 2010;32(6):736–42.
[13] Varadarajan M, Swarup KS. Solving multi-objective optimal power flow
using differential evolution. IEE Proc Gener Transm Distrib 2008;2(5):720–
30.
[14] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast elitist multiobjective
genetic algorithm: NSGA-II. IEEE Trans Evol Comput 2002;6(2):182–97.
[15] Deb K. Multiobjective optimization using evolutionary algorithms.
Chichester (UK): Wiley; 2001.
[16] Luo B, Zheng J, Xie J, Wu J. Dynamic crowding distance – a new
diversity maintenance strategy for MOEAs. In: Proceedings of the IEEE
international conference on natural computation; October 2008. p. 580–5.

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